Dry magnetic assisted impaction mixing of sub-micron boron and barium chromate for a time delay composition

The objective of this investigation is to improve the mixture homogeneity of, Boron and Barium Chromate, a delay composition called T-10, using a novel dry mixing technique, , called the Magnetic Assisted Impaction Mixing (MAIM). Two additional mixing methods, ultra-sonic mixing and shaker mixing (dry and wet), were used for comparative analysis, to evaluate the mixing effectiveness of the MAIM which was also compared to two Navy mixed T-l0 samples. Characterization of the homogeneity is described by how well boron is effectively distributed throughout the mixture, which is a challenging task since the mixture's composition is 3% Boron versus 97% of Barium Chromate by weight, because boron has a low energy signature in energy dispersive x-ray (EDX) analysis. Qualitative mixing characterization was done using Field Emission-Scanning Electron Microscope (FESEM) with an ESB detector which provides higher Z contrast at extremely low accelerating voltage, resulting in higher spatial resolution and other systems such as Particle Size Analyzer (LS-230). The results indicate that the new MAIM techniques produces a mixture with a significantly improved homogeneity and a narrow particle size distribution due to its ability to break down large powder aggregates at a fraction of the time compared to the current Navy wet mixing processes, eliminates downstream processing associated with drying, and shows improved performance in majority of combustion propagation bum tests.

Name thesis:degree_name

Master of Science in Mechanical Engineering - (M.S.)

Discipline thesis:degree_discipline

Mechanical Engineering

Year

2006

Author dc:creator

• Barrow, Ryan

Contributors dc:contributor

• Rajesh N. Dave
• Edward L. Dreyzin
• Mirko Schoenitz

• Born and barium chromate
• Dry mixing
• Magnetic Assisted Impaction Mixing
• Mechanical Engineering

Repository record dc:identifier

https://digitalcommons.njit.edu/theses/420

OAI identifier oai:identifier

oai:digitalcommons.njit.edu:theses-1419